See the project wiki.
See issues.
Sequence of scripts:
devtoolsloadPkg functionmakeMetadata function## Sourcing https://gist.githubusercontent.com/benjamin-chan/3b59313e8347fffea425/raw/c03cd15480a6444399ff5f34892f5911d6e12b44/loadPkg.R
## SHA-1 hash of file is 0bb1bb041c1dda15ee613db701d0c5784d1196a5
## Sourcing https://gist.githubusercontent.com/benjamin-chan/091209ab4eee1f171540/raw/156a5e29111d0da6ec5693f5a881628e10fb9613/makeMetadata.R
## SHA-1 hash of file is 8b07ca14d3606ec83f76e636e7f9088e73a003b0Replicate data from Ono, Miyuki, et al. “A Meta-Analysis of Cognitive Impairment and Decline Associated with Adjuvant Chemotherapy in Women with Breast Cancer.” Front Oncol. 2015; 5: 59.
Data file was requested and received from the co-author, James Ogilvie, in October 2015.
The DOMAINFORMETAkvd field (column AG) was coded by Kathleen Van Dyk KVanDyk@mednet.ucla.edu.
From: Van Dyk, Kathleen [KVanDyk@mednet.ucla.edu]
Sent: Tuesday, November 03, 2015 5:08 PM
To: Ayse Tezcan
Cc: Benjamin Chan
Subject: RE: Cognitive impairment draft paper
Hi,
Attached is the Ono spreadsheet with a new column with my suggestions for
domains and domains for each Ahles test is in sheet 2. I've highlighted tests
that we may want to exclude if we want to consistently keep one or two
measures per test. Ben --- does it matter statistically if there is more than
one measure from the same test (for example delayed recall and delayed
recognition) in the same domain? In almost every case we have total and delay
for memory tests but if we add in more measures (Trial 6, Supraspan,
Recognition) does this confound analyses because these are likely highly
correlated measures within the same test? Would all of the studies need to
use the same measures in each test (i.e., every study uses Total and Delay)?
I might not be asking this clearly --- let me know what you think.Read data file.
f <- sprintf("%s/%s", pathIn, "Requested Chemo Data domains kvd 11.19.15 2.xlsx")
echoFile(f)## File: StudyDocuments/CognitiveImpairment/Requested Chemo Data domains kvd 11.19.15 2.xlsx
## Modification date: 2015-11-19 10:55:10
## File size: 178.9 KBD0 <- read.xlsx(f, sheet=1, check.names=TRUE)
D0 <- data.table(D0)Show a map of the column names and locations.
colNames <- data.frame(colNum = 1:ncol(D0),
colCell = c(LETTERS,
sprintf("%s%s", LETTERS[1], LETTERS),
sprintf("%s%s", LETTERS[2], LETTERS),
sprintf("%s%s", LETTERS[3], LETTERS))[1:ncol(D0)],
varName = names(D0))
colNames## colNum colCell varName
## 1 1 A First.Auth
## 2 2 B Study.Ref
## 3 3 C Pub.Year
## 4 4 D Cog.Test
## 5 5 E DOMAIN.FOR.META..kvd.
## 6 6 F Journal
## 7 7 G Pre.Meta
## 8 8 H Design
## 9 9 I Comp.Grp
## 10 10 J Healthy_GROUP
## 11 11 K Tx.Grp
## 12 12 L Pre.Post.Time.Interval
## 13 13 M Time.SD
## 14 14 N Tx.N
## 15 15 O Ctl.N
## 16 16 P Total.N
## 17 17 Q Tx.Age
## 18 18 R Tx.Age.SD
## 19 19 S Ctl.Age
## 20 20 T Ctl.Age.SD
## 21 21 U Tx.IQ
## 22 22 V Tx.IQ.SD
## 23 23 W Ctl.IQ
## 24 24 X Ctl.IQ.SD
## 25 25 Y IQ.Note
## 26 26 Z Tx.EDU
## 27 27 AA Tx.EDU.SD
## 28 28 AB Ctl.EDU
## 29 29 AC Ctl.EDU.SD
## 30 30 AD EDU.Note
## 31 31 AE Tx.Chem.Time
## 32 32 AF Tx.Chem.Time.SD
## 33 33 AG Cognitive.Domain.Primary
## 34 34 AH Score.Typ
## 35 35 AI Tx.M
## 36 36 AJ Tx.SD
## 37 37 AK Ctl.M
## 38 38 AL Ctl.SD
## 39 39 AM Direct.Notes
## 40 40 AN X1.X2
## 41 41 AO Tx.N.1
## 42 42 AP Ctl.N.1
## 43 43 AQ Tx.SD.2
## 44 44 AR Ctl.SD.2
## 45 45 AS Spooled
## 46 46 AT Cohen.s.d
## 47 47 AU Hedges.g
## 48 48 AV Var1
## 49 49 AW Var2
## 50 50 AX Variance
## 51 51 AY Standard.Error
## 52 52 AZ Weight
## 53 53 BA w.ES
## 54 54 BB w.ES.2
## 55 55 BC w.2
## 56 56 BD StudyES
## 57 57 BE StudySE
## 58 58 BF z
## 59 59 BG LowerCI
## 60 60 BH UpperCI
## 61 61 BI Q
## 62 62 BJ df
## 63 63 BK Q.Critical
## 64 64 BL Q.Sig...p..05.
## 65 65 BM RANDOM.EFFECT
## 66 66 BN RE_w
## 67 67 BO w.ES.1
## 68 68 BP w.ES.2.1
## 69 69 BQ w.2.1
## 70 70 BR StudyES.1
## 71 71 BS StudySE.1
## 72 72 BT z.1
## 73 73 BU LowerCI.1
## 74 74 BV UpperCI.1
## 75 75 BW Q.1
## 76 76 BX df.1
## 77 77 BY Q.Critical.1
## 78 78 BZ Q.Sig...p..05..1
## 79 79 CA I.2.Fixed
## 80 80 CB I.2.RandomPut the summary rows in a separate data table, DOno.
DOno <- D0[is.na(First.Auth) & !is.na(Weight), c(52:ncol(D0)), with=FALSE]Put the instrument-level rows in a separate data table, D. Only keep the columns needed to calculate fixed and random effects statistics.
The RANDOM.EFFECT column was specific to the Ono analysis. The value in the Ono spreadsheet will be different for our use.
From: James Ogilvie [j.ogilvie@griffith.edu.au]
Sent: Sunday, October 18, 2015 5:42 PM
To: Benjamin Chan
Cc: 'jamelnikow@ucdavis.edu'; 'm.ono@griffith.edu.au'; 'd.shum@griffith.edu.au'; Ayse Tezcan (aztezcan@ucdavis.edu); Meghan Soulsby (masoulsby@ucdavis.edu)
Subject: Re: Fwd: request for data from your recently published meta-analysis
Hi Benjamin,
Thanks for contacting me regarding this issue. I had wondered whether Dr. Melnikow had received the data I had sent, as I had not received confirmation of my email containing the data.
These are very good questions! It took me a while to get my head around the random effect model when performing this analysis. I am attaching an article that I found very useful in coming to terms with the model - hopefully you will find this useful too.
To answer your questions, is a constant across a pool of studies that you wish to examine and generate summary/aggregate statistics (e.g., grand mean effect size). Therefore, the value of the constant will change depending on the the specific pool of studies examined. It is calculated across the total pool of studies.
is the total Q statistic (assessing heterogeneity) that is calculated across ALL studies and relates to the grand mean effect size. It is not the same as the Q statistic in column BH. There is a Q statistic for each study (this is the Q in column BH), as well as a Q statistic for all studies pooled together (this being the statistic). The formula for calculating the Q statistic are provided in the pdf I've attached titled "Heterogeneity in MA".
As I've mentioned, the value of is specific to the pool of studies you are examining. Therefore, the value to calculate effect sizes according to a random effects model will be different for your analyses - assuming you have a different pool of studies that you are including in the analyses. Given this, the value in column BL needs to be updated by you to be specific to the pool of studies you are looking at.importantVar <- c(1, 9:12, 14:17, 19, 35:39, 65, 33, 4, 5, 34)
authors <- c("Bender", "Collins", "Jenkins", "Wefel")
D <- D0[First.Auth %in% authors, importantVar, with=FALSE]
setnames(D,
names(D),
c("author",
"comparisonGroup",
"healthyGroup",
"treatmentGroup",
"timeDays",
"nGroup1",
"nGroup2",
"nTotal",
"ageGroup1",
"ageGroup2",
"meanGroup1",
"sdGroup1",
"meanGroup2",
"sdGroup2",
"direction",
"randomEffect", # Keep the value from Ono for verification purposes; do not use for analysis
gsub("\\.", "", names(D0)[c(33, 4, 5, 34)])))
setnames(D, "DOMAINFORMETAkvd", "CognitiveDomainForMetaAnalysis")The data in the received file is in the form of longitudinal means and standard deviations.
D## author comparisonGroup healthyGroup
## 1: Bender One week after completion of chemotherapy 1
## 2: Bender One week after completion of chemotherapy 1
## 3: Bender One week after completion of chemotherapy 1
## 4: Bender One week after completion of chemotherapy 1
## 5: Bender One week after completion of chemotherapy 1
## ---
## 126: Wefel Chemotherapy one year after 1
## 127: Wefel NA NA
## 128: Wefel NA NA
## 129: Wefel NA NA
## 130: Wefel NA NA
## treatmentGroup timeDays nGroup1 nGroup2 nTotal ageGroup1
## 1: Chemotherapy + Tamoxifen 182.625 19 12 31 44.13
## 2: Chemotherapy 182.625 19 15 34 40.11
## 3: Chemotherapy + Tamoxifen 182.625 19 12 31 44.13
## 4: Chemotherapy 182.625 19 15 34 40.11
## 5: Chemotherapy + Tamoxifen 182.625 19 12 31 44.13
## ---
## 126: NA 547.863 18 14 32 45.40
## 127: NA NA NA NA NA NA
## 128: NA NA NA NA NA NA
## 129: NA NA NA NA NA NA
## 130: NA NA NA NA NA NA
## ageGroup2 meanGroup1 sdGroup1 meanGroup2 sdGroup2 direction
## 1: NA 10.00 0.77 12.38 0.53 Lower worse
## 2: NA 10.79 0.59 11.96 0.46 Lower worse
## 3: NA 51.00 2.45 54.90 5.06 Lower worse
## 4: NA 56.47 2.14 56.89 1.75 Lower worse
## 5: NA 10.47 0.81 11.96 0.42 Lower worse
## ---
## 126: NA 11.44 2.85 12.71 3.77 Lower worse
## 127: NA NA NA NA NA NA
## 128: NA NA NA NA NA NA
## 129: NA NA NA NA NA NA
## 130: NA NA NA NA NA NA
## randomEffect CognitiveDomainPrimary CogTest
## 1: 0.161462 LTM RAVL delayed recall
## 2: 0.161462 LTM RAVL delayed recall
## 3: 0.161462 LTM RAVL total score
## 4: 0.161462 LTM RAVL total score
## 5: 0.161462 LTM RAVL trial 6
## ---
## 126: 0.161462 Visuospatial WAIS-R block design
## 127: NA NA VSRT Long-Term Storage
## 128: NA NA NVSRT Delayed Recall
## 129: NA NA VSRT Delayed Recall
## 130: NA NA NVSRT Delayed Recall
## CognitiveDomainForMetaAnalysis ScoreTyp
## 1: Verbal Memory Raw score
## 2: Verbal Memory Raw score
## 3: Verbal Memory Raw score
## 4: Verbal Memory Raw score
## 5: Verbal Memory Raw score
## ---
## 126: Visuospatial Scaled scores
## 127: Verbal Memory NA
## 128: Visual Memory NA
## 129: Verbal Memory NA
## 130: Visual Memory NAReplicate spreadsheet calculations.
D <- D[direction == "Lower worse",
`:=` (diffMean = meanGroup2 - meanGroup1)]
D <- D[direction == "Greater worse",
`:=` (diffMean = meanGroup1 - meanGroup2)]
D <- D[,
`:=` (sdPooled = sqrt((((nGroup1 - 1) * (sdGroup1 ^ 2)) +
((nGroup2 - 1) * (sdGroup2 ^ 2))) /
(nGroup1 + nGroup2 - 2)))]
D <- D[,
`:=` (cohenD = diffMean / sdPooled)]
D <- D[,
`:=` (hedgesG = cohenD * (1 - (3 / ((4 * nTotal) - 9))))]
D <- D[,
`:=` (var1 = (nGroup1 + nGroup2) / (nGroup1 * nGroup2),
var2 = hedgesG ^ 2 / (2 * (nGroup1 + nGroup2)))]
D <- D[,
`:=` (variance = var1 + var2)]
D <- D[,
`:=` (se = sqrt(variance),
weightFE = 1 / variance)]
D <- D[,
`:=` (effSizeWeightedFE = weightFE * hedgesG)]
D <- D[, weightRE := 1 / (variance + randomEffect)]
D <- D[, effSizeWeightedRE := weightRE * hedgesG]Calculate fixed effects statisitcs.
DFixed <- D[!is.na(nTotal),
.(df = .N,
sumWeights = sum(weightFE),
effSize = sum(effSizeWeightedFE) / sum(weightFE),
se = sqrt(1 / sum(weightFE)),
sumEffSizeWeighted = sum(effSizeWeightedFE),
ssEffSizeWeighted = sum(weightFE * hedgesG ^ 2),
ssWeights = sum(weightFE ^ 2)),
.(author, timeDays)]
DFixed <- DFixed[,
`:=` (z = effSize / se,
lowerCI = effSize + qnorm(0.025) * se,
upperCI = effSize + qnorm(0.975) * se,
Q = ssEffSizeWeighted - (sumEffSizeWeighted ^ 2 / sumWeights),
criticalValue = qchisq(0.05, df, lower.tail=FALSE))]
DFixed <- DFixed[,
`:=` (pvalue = pchisq(Q, df, lower.tail=FALSE),
Isq = 100 * ((Q - df) / Q))]Check if my calculations agree with Ono’s.
isCheckFixedPassed <- all.equal(DOno[, .(StudyES, z, Q)],
DFixed[, .(effSize, z, Q)],
check.names=FALSE)
message(sprintf("Do my FIXED effect statistic calculations agree with Ono's? %s",
isCheckFixedPassed))## Do my FIXED effect statistic calculations agree with Ono's? TRUEprint(xtable(DFixed), type="html")| author | timeDays | df | sumWeights | effSize | se | sumEffSizeWeighted | ssEffSizeWeighted | ssWeights | z | lowerCI | upperCI | Q | criticalValue | pvalue | Isq | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Bender | 182.62 | 16 | 96.80 | 1.02 | 0.10 | 98.53 | 241.49 | 626.88 | 10.01 | 0.82 | 1.22 | 141.21 | 26.30 | 0.00 | 88.67 |
| 2 | Bender | 547.50 | 16 | 63.53 | 0.55 | 0.13 | 34.76 | 285.90 | 290.59 | 4.36 | 0.30 | 0.79 | 266.88 | 26.30 | 0.00 | 94.00 |
| 3 | Collins | 537.90 | 23 | 604.56 | 0.21 | 0.04 | 124.90 | 39.49 | 15893.07 | 5.08 | 0.13 | 0.29 | 13.69 | 35.17 | 0.94 | -68.01 |
| 4 | Collins | 146.50 | 23 | 607.30 | 0.10 | 0.04 | 58.14 | 17.63 | 16035.80 | 2.36 | 0.02 | 0.18 | 12.07 | 35.17 | 0.97 | -90.58 |
| 5 | Jenkins | 364.00 | 14 | 592.05 | 0.08 | 0.04 | 47.58 | 23.62 | 25038.13 | 1.96 | -0.00 | 0.16 | 19.79 | 23.68 | 0.14 | 29.27 |
| 6 | Jenkins | 28.00 | 14 | 593.46 | 0.03 | 0.04 | 19.46 | 12.29 | 25157.32 | 0.80 | -0.05 | 0.11 | 11.65 | 23.68 | 0.63 | -20.17 |
| 7 | Wefel | 182.62 | 10 | 89.09 | 0.18 | 0.11 | 15.73 | 5.24 | 793.76 | 1.67 | -0.03 | 0.38 | 2.47 | 18.31 | 0.99 | -305.57 |
| 8 | Wefel | 547.86 | 10 | 79.52 | 0.26 | 0.11 | 20.75 | 8.62 | 632.66 | 2.33 | 0.04 | 0.48 | 3.20 | 18.31 | 0.98 | -212.29 |
Calculate random effects statisitcs.
DRandom <- D[!is.na(nTotal),
.(df = .N,
sumWeights = sum(weightRE),
ssEffSizeWeighted = sum(weightRE * hedgesG ^ 2),
ssWeights = sum(weightRE ^ 2),
sumEffSizeWeighted = sum(effSizeWeightedRE),
effSize = sum(effSizeWeightedRE) / sum(weightRE),
se = sqrt(1 / sum(weightRE))),
.(author, timeDays)]
DRandom <- DRandom[,
`:=` (z = effSize / se,
lowerCI = effSize + qnorm(0.025) * se,
upperCI = effSize + qnorm(0.975) * se,
Q = ssEffSizeWeighted - (sumEffSizeWeighted ^ 2 / sumWeights),
criticalValue = qchisq(0.05, df, lower.tail=FALSE))]
DRandom <- DRandom[,
`:=` (pvalue = pchisq(Q, df, lower.tail=FALSE),
Isq = 100 * ((Q - df) / Q))]Check if my calculations agree with Ono’s.
isCheckRandomPassed <- all.equal(DOno[, c(19, 21, 24), with=FALSE],
DRandom[, .(effSize, z, Q)],
check.names=FALSE)
message(sprintf("Do my RANDOM effect statistic calculations agree with Ono's? %s",
isCheckRandomPassed))## Do my RANDOM effect statistic calculations agree with Ono's? TRUEprint(xtable(DRandom), type="html")| author | timeDays | df | sumWeights | ssEffSizeWeighted | ssWeights | sumEffSizeWeighted | effSize | se | z | lowerCI | upperCI | Q | criticalValue | pvalue | Isq | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Bender | 182.62 | 16 | 48.04 | 137.29 | 147.43 | 53.60 | 1.12 | 0.14 | 7.73 | 0.83 | 1.40 | 77.50 | 26.30 | 0.00 | 79.35 |
| 2 | Bender | 547.50 | 16 | 37.25 | 199.95 | 92.56 | 26.23 | 0.70 | 0.16 | 4.30 | 0.38 | 1.03 | 181.48 | 26.30 | 0.00 | 91.18 |
| 3 | Collins | 537.90 | 23 | 115.28 | 7.62 | 577.83 | 23.96 | 0.21 | 0.09 | 2.23 | 0.03 | 0.39 | 2.64 | 35.17 | 1.00 | -770.38 |
| 4 | Collins | 146.50 | 23 | 115.38 | 3.38 | 578.84 | 11.11 | 0.10 | 0.09 | 1.03 | -0.09 | 0.28 | 2.31 | 35.17 | 1.00 | -895.09 |
| 5 | Jenkins | 364.00 | 14 | 75.63 | 3.04 | 408.57 | 6.11 | 0.08 | 0.11 | 0.70 | -0.14 | 0.31 | 2.54 | 23.68 | 1.00 | -450.68 |
| 6 | Jenkins | 28.00 | 14 | 75.65 | 1.57 | 408.83 | 2.50 | 0.03 | 0.11 | 0.29 | -0.19 | 0.26 | 1.49 | 23.68 | 1.00 | -840.71 |
| 7 | Wefel | 182.62 | 10 | 36.53 | 2.16 | 133.47 | 6.48 | 0.18 | 0.17 | 1.07 | -0.15 | 0.50 | 1.01 | 18.31 | 1.00 | -890.02 |
| 8 | Wefel | 547.86 | 10 | 34.81 | 3.79 | 121.21 | 9.12 | 0.26 | 0.17 | 1.55 | -0.07 | 0.59 | 1.41 | 18.31 | 1.00 | -611.34 |
Exclude tests Kathleen determined to be not useful.
From: Van Dyk, Kathleen [KVanDyk@mednet.ucla.edu]
Sent: Thursday, November 19, 2015 10:22 AM
To: Benjamin Chan
Cc: Ayse Tezcan
Subject: RE: Cognitive impairment draft paper
Hi Ben,
Ok --- attached is the Ono spreadsheet with my suggested domains. I did
strikethrough for the measures we probably shouldn't include at all in the
domains to keep it somewhat uniform across tests (i.e., some folks used Trial
1 from a list-learning test, some just used Total and Delay, etc.).strikethrough <- c("RAVL trial 6",
"CVLT Trial 1",
"RVLT trial 1",
"AVLT supraspan")
D <- D[!(CogTest %in% strikethrough)]Domains and tests.
D[,
.N,
.(CognitiveDomainForMetaAnalysis,
CognitiveDomainPrimary,
CogTest)][order(CognitiveDomainForMetaAnalysis,
CognitiveDomainPrimary,
CogTest)]## CognitiveDomainForMetaAnalysis CognitiveDomainPrimary
## 1: Attn/Wkg Mem/Concentration Attention
## 2: Attn/Wkg Mem/Concentration Attention
## 3: Attn/Wkg Mem/Concentration Attention
## 4: Attn/Wkg Mem/Concentration Attention
## 5: Attn/Wkg Mem/Concentration Attention
## 6: Attn/Wkg Mem/Concentration Attention
## 7: Attn/Wkg Mem/Concentration Attention
## 8: Attn/Wkg Mem/Concentration Attention
## 9: Attn/Wkg Mem/Concentration Attention
## 10: Attn/Wkg Mem/Concentration Attention
## 11: Attn/Wkg Mem/Concentration Attention
## 12: Attn/Wkg Mem/Concentration Executive Function
## 13: Attn/Wkg Mem/Concentration LTM
## 14: Attn/Wkg Mem/Concentration STM
## 15: Attn/Wkg Mem/Concentration STM
## 16: Attn/Wkg Mem/Concentration STM
## 17: Attn/Wkg Mem/Concentration STM
## 18: Attn/Wkg Mem/Concentration STM
## 19: Attn/Wkg Mem/Concentration STM
## 20: Exec Fxn Executive Function
## 21: Exec Fxn Executive Function
## 22: Exec Fxn Executive Function
## 23: Exec Fxn Executive Function
## 24: Exec Fxn Language
## 25: Information Proc Speed Attention
## 26: Information Proc Speed Attention
## 27: Information Proc Speed Processing
## 28: Information Proc Speed Processing
## 29: Information Proc Speed Processing
## 30: Motor Speed Motor
## 31: Motor Speed Motor
## 32: Motor Speed Motor
## 33: Verbal Ability/Language Executive Function
## 34: Verbal Ability/Language Executive Function
## 35: Verbal Ability/Language Language
## 36: Verbal Memory LTM
## 37: Verbal Memory LTM
## 38: Verbal Memory LTM
## 39: Verbal Memory LTM
## 40: Verbal Memory LTM
## 41: Verbal Memory LTM
## 42: Verbal Memory LTM
## 43: Verbal Memory STM
## 44: Verbal Memory STM
## 45: Verbal Memory NA
## 46: Verbal Memory NA
## 47: Visual Memory LTM
## 48: Visual Memory LTM
## 49: Visual Memory LTM
## 50: Visual Memory LTM
## 51: Visual Memory LTM
## 52: Visual Memory STM
## 53: Visual Memory STM
## 54: Visual Memory NA
## 55: Visuospatial Visuospatial
## 56: Visuospatial Visuospatial
## CognitiveDomainForMetaAnalysis CognitiveDomainPrimary
## CogTest N
## 1: WAIS-III -Arithmetic 2
## 2: PASAT number correct 2
## 3: TMT part A time 2
## 4: Trails A 2
## 5: WAIS-III Digit span 2
## 6: WAIS-R arithmetic 2
## 7: WAIS-R digit span 2
## 8: WMS-III digit span backwards 2
## 9: WMS-III digit span forward 2
## 10: WMS-III spatial span backwards 2
## 11: WMS-III spatial span forwards 2
## 12: Consonant trigrams 2
## 13: Spatial span: WMS-III 2
## 14: 4WSTM 15 sec 4
## 15: 4WSTM 30 sec 4
## 16: 4WSTM 5 sec 4
## 17: Letter-number sequencing: WAIS-III 1
## 18: WAIS-III Letter-number sequencing 1
## 19: WMS-III letter number sequencing 2
## 20: Stroop 2
## 21: TMT part B time 2
## 22: Trails B 2
## 23: WCST sorts divided by trials 2
## 24: WAIS-R similarities 2
## 25: WAIS-III Digit Symbol Coding 2
## 26: WAIS-R digit symbol 2
## 27: Letter cancellation 2
## 28: Symbol search: WAIS-III 1
## 29: WAIS-III Symbol search 1
## 30: Grooved Peg Board time 2
## 31: Grooved pegboard dominant hand 2
## 32: Grooved pegboard nondominant hand 2
## 33: Verbal Fluency FAS number correct 2
## 34: Verbal fluency COWAT correct 2
## 35: Boston Naming Test number correct 2
## 36: AVLT delayed 2
## 37: CVLT delayed recall 2
## 38: CVLT delayed recognition 2
## 39: RAVL delayed recall 4
## 40: RAVL total score 4
## 41: WMS-III Logical memory II 2
## 42: WMS-III Story delayed recall 2
## 43: AVLT total 2
## 44: WMS-III Story immediate recall 2
## 45: VSRT Delayed Recall 1
## 46: VSRT Long-Term Storage 1
## 47: Complex figure delayed 2
## 48: RCF delayed recall 4
## 49: RVLT delayed recall 2
## 50: RVLT delayed recognition 2
## 51: WMS-III Family pictures II 2
## 52: Complex figure immediate 2
## 53: RCF immediate recall 4
## 54: NVSRT Delayed Recall 2
## 55: WAIS-III Block design 2
## 56: WAIS-R block design 2
## CogTest NSave working data tables to file if the integrity checks passed. I don’t need to save DOno since the integrity checks passed.
metadataD = makeMetadata(D)
metadataDFixed = makeMetadata(DFixed)
metadataDRandom = makeMetadata(DRandom)
if (isCheckFixedPassed & isCheckRandomPassed) {
f <- "Ono.RData"
save(D,
metadataD,
DFixed,
metadataDFixed,
DRandom,
metadataDRandom,
file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))
} else {
warning(sprinf("Integrity checks failed.\n%s not saved.", f))
}## Ono.RData saved on: 2015-11-25 15:02:04
## File size: 66.808 KBRead data from Ahles TA, et al. “Longitudinal Assessment of Cognitive Changes Associated With Adjuvant Treatment for Breast Cancer: Impact of Age and Cognitive Reserve.” J Clin Oncol. 2010 Oct 10; 28(29): 4434-4440.
Data file was requested and received from the co-author, Yuelin Li, in October 2015.
Read data file (text format).
f <- sprintf("%s/%s", pathIn, "Soulsby_means.txt")
echoFile(f)## File: StudyDocuments/CognitiveImpairment/Soulsby_means.txt
## Modification date: 2015-10-30 08:21:29
## File size: 36.1 KBD <- fread(f, sep="|")The data in the received file is in the form of longitudinal means and standard deviations.
D## txgrp ptime NObs Variable
## 1: chemo baseline 60 AN_NAMES_z_adj
## 2: chemo baseline 60 BD_RAW_z_adj
## 3: chemo baseline 60 CFL_z_adj
## 4: chemo baseline 60 CVLT_z_adj
## 5: chemo baseline 60 DCCSORT_z_adj
## ---
## 416: control 2yr 39 READ_RAW_z_adj
## 417: control 2yr 39 VIGCR_z_adj
## 418: control 2yr 39 VIGFP_z_adj
## 419: control 2yr 39 VIGRT_z_adj
## 420: control 2yr 39 VOC_RAW_z_adj
## Label N Mean Median
## 1: DKEFS Verbal Fluency: anival or clothing and names 60 -0.0734 -0.2300
## 2: WASI: Block Design 60 -0.2050 -0.2220
## 3: DKEFS Verbal Fluency 60 -0.0211 -0.0655
## 4: CVLT-2: Trials 1-5 Total 60 -0.1900 -0.2170
## 5: DKEFS Card Sorting: Confirmed Correct Sorts 60 0.0652 0.2660
## ---
## 416: WRAT-3 Reading Score 39 0.2470 0.3730
## 417: CPT: Vigilance, Correct Responses 38 0.1420 0.4320
## 418: CPT: Vigilance, False Positives 38 -0.3970 -0.4710
## 419: CPT: Vigilance, Reaction Time 38 0.1140 0.3050
## 420: WASI: Vocabulary 39 0.1130 0.3870
## StdDev
## 1: 1.263
## 2: 0.939
## 3: 1.036
## 4: 1.131
## 5: 1.059
## ---
## 416: 1.179
## 417: 0.728
## 418: 0.355
## 419: 0.987
## 420: 0.986Study design.
D[, .(nrows = .N, totalN = sum(N)), .(txgrp, ptime)]## txgrp ptime nrows totalN
## 1: chemo baseline 35 2056
## 2: chemo posttx 35 1886
## 3: chemo 1yr 35 1677
## 4: chemo 2yr 35 1549
## 5: chemo no baseline 35 2432
## 6: chemo no posttx 35 2321
## 7: chemo no 1yr 35 2237
## 8: chemo no 2yr 35 2138
## 9: control baseline 35 1522
## 10: control posttx 35 1478
## 11: control 1yr 35 1447
## 12: control 2yr 35 1346Map ptime to months after treatment. Ahles TA, et al. reports results in terms of
As far as I can tell, values of ptime map to these, although seemingly imprecise.
D[ptime == "baseline", monthsPostTx := 0]
D[ptime == "posttx", monthsPostTx := 1]
D[ptime == "1yr", monthsPostTx := 6]
D[ptime == "2yr", monthsPostTx := 18]Exclude
D <- D[txgrp == "chemo"]Instruments.
D[, .N, .(Variable, Label)]## Variable Label N
## 1: AN_NAMES_z_adj DKEFS Verbal Fluency: anival or clothing and names 4
## 2: BD_RAW_z_adj WASI: Block Design 4
## 3: CFL_z_adj DKEFS Verbal Fluency 4
## 4: CVLT_z_adj CVLT-2: Trials 1-5 Total 4
## 5: DCCSORT_z_adj DKEFS Card Sorting: Confirmed Correct Sorts 4
## 6: DCOLT_z_adj DKEFS Stroop: Color Patch Naming 4
## 7: DCSC_z_adj DKEFS Verbal Fluency: Switching Fruits/Veget 4
## 8: DCWT_z_adj DKEFS Stroop: Color-Word 4
## 9: DFSDES_z_adj DKEFS Card Sorting: Free Sorting 4
## 10: DISCR_z_adj CPT: Distractibility, Correct Responses 4
## 11: DISFP_z_adj CPT: Distractibility, False Positives 4
## 12: DISRT_z_adj CPT: Distractibility, Reaction Time 4
## 13: DRECDES_z_adj DKEFS: Card Sorting, Sort Recognition 4
## 14: DST_z_adj DKEFS: Stroop, Set Shifting 4
## 15: DSY_RAW_z_adj CVLT-2: Digit Symbol 4
## 16: DTR1SC_z_adj DKEFS Trails: Visual Scanning in Seconds 4
## 17: DTR2SC_z_adj DKEFS Trails: Number Sequencing, sec 4
## 18: DTR3SC_z_adj DKEFS Trails: Letter Sequencing, sec 4
## 19: DTR4SC_z_adj DKEFS Trails: Number-Letter Switching, sec 4
## 20: DTR5SC_z_adj DKEFS Trails: Motor Speed, sec 4
## 21: DWRDT_z_adj DKEFS Stroop: Word Reading, sec 4
## 22: FACE1_z_adj Wechsler Memory Scale-3: Faces I 4
## 23: FACE2_z_adj Wechsler Memory Scale-3: Faces II 4
## 24: GROOVEL_z_adj Grooved Pegboard Test: Left Hand, sec 4
## 25: GROOVER_z_adj Grooved Pegboard Test: Right Hand, sec 4
## 26: LD_z_adj CVLT-2: Long Delay Free Recall 4
## 27: LM1_z_adj Wechsler Memory Scale-3: Logical Memory I 4
## 28: LM2_z_adj Wechsler Memory Scale-3: Logical Memory II 4
## 29: RAO2_z_adj PASAT (Rao): 2 second pacing 4
## 30: RAO3_z_adj PASAT (Rao): 3 second pacing 4
## 31: READ_RAW_z_adj WRAT-3 Reading Score 4
## 32: VIGCR_z_adj CPT: Vigilance, Correct Responses 4
## 33: VIGFP_z_adj CPT: Vigilance, False Positives 4
## 34: VIGRT_z_adj CPT: Vigilance, Reaction Time 4
## 35: VOC_RAW_z_adj WASI: Vocabulary 4
## Variable Label NMerge Kathleen’s KVanDyk@mednet.ucla.edu domain assignments.
From: Van Dyk, Kathleen [KVanDyk@mednet.ucla.edu]
Sent: Tuesday, November 03, 2015 5:08 PM
To: Ayse Tezcan
Cc: Benjamin Chan
Subject: RE: Cognitive impairment draft paper
Hi,
Attached is the Ono spreadsheet with a new column with my suggestions for
domains and domains for each Ahles test is in sheet 2. I've highlighted tests
that we may want to exclude if we want to consistently keep one or two
measures per test. Ben --- does it matter statistically if there is more than
one measure from the same test (for example delayed recall and delayed
recognition) in the same domain? In almost every case we have total and delay
for memory tests but if we add in more measures (Trial 6, Supraspan,
Recognition) does this confound analyses because these are likely highly
correlated measures within the same test? Would all of the studies need to
use the same measures in each test (i.e., every study uses Total and Delay)?
I might not be asking this clearly --- let me know what you think.f <- sprintf("%s/%s", pathIn, "Requested Chemo Data domains kvd 11.19.15 2.xlsx")
echoFile(f)## File: StudyDocuments/CognitiveImpairment/Requested Chemo Data domains kvd 11.19.15 2.xlsx
## Modification date: 2015-11-19 10:55:10
## File size: 178.9 KBD0 <- read.xlsx(f, sheet=2, check.names=TRUE)
D0 <- data.table(D0)
CognitiveDomainForMetaAnalysis <- D0[!is.na(DOMAIN.FOR.META..kvd.), DOMAIN.FOR.META..kvd.]
lookup <- cbind(D[, .N, .(Variable, Label)], CognitiveDomainForMetaAnalysis)[, .(Variable, CognitiveDomainForMetaAnalysis)]
D <- merge(lookup, D, by="Variable")Save working data tables to file.
metadata <- makeMetadata(D)
f <- "Ahles.RData"
save(D, metadata, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## Ahles.RData saved on: 2015-11-25 15:02:04
## File size: 21.132 KBRead data from Tager, FA, et al. “The cognitive effects of chemotherapy in post-menopausal breast cancer patients: a controlled longitudinal study.” Breast Cancer Res Treat. 2010 Aug;123(1):25-34.
Data file was requested and received from the co-author, Paula S. McKinley, on November 20, 2015.
Read data file (SPSS format).
f <- sprintf("%s/%s", pathIn, "Tager_DataForMetaAnalysis.sav")
echoFile(f)## File: StudyDocuments/CognitiveImpairment/Tager_DataForMetaAnalysis.sav
## Modification date: 2015-11-23 09:18:36
## File size: 102.7 KBD <- read_sav(f)
D <- data.table(D)
D <- D[,
`:=` (session = factor(session,
levels = 1:4,
labels = c("Pre-surgery",
"Post surgery before treatment",
"Post treatment/6mths post surgery",
"6 month follow-up")),
chemoyn = factor(chemoyn,
levels= 0:1,
labels = c("No", "Yes")),
CTregmen = factor(CTregmen,
levels = 1:3,
labels = c("AC",
"ACT",
"CMF")),
tx = factor(tx,
levels = 0:11,
labels = c("None",
"Chemo",
"Radiation",
"Tamoxifen",
"Arimadex",
"Chemo + Radiation",
"Chemo + Tamoxifen",
"Chemo + Arimadex",
"Radiation + Tamoxifen",
"Radiation + Arimadex",
"Chemo + Radiation + Tamoxifen",
"Chemo + Radiation + Arimadex")))]Check data.
D[, .N, .(chemoyn, CTregmen)]## chemoyn CTregmen N
## 1: No NA 89
## 2: Yes CMF 24
## 3: Yes ACT 40
## 4: Yes AC 21D[, .N, .(chemoyn, chemowks)]## chemoyn chemowks N
## 1: No 88 89
## 2: Yes 24 39
## 3: Yes 16 14
## 4: Yes 12 8
## 5: Yes 28 3
## 6: Yes 18 3
## 7: Yes 8 12
## 8: Yes 14 6Keep z-score variables for these instruments.
measures <- c("tapdomz",
"tapndomz",
"pegdomz",
"pegndomz",
"cowz",
"bntz",
"trlaz",
"trlbz",
"dsymz",
"dspaz",
"aritz",
"numz",
"reyz",
"bustotz",
"bvrcoz")Melt data.
D <- melt(D,
id.vars = c("subid", "session", "chemoyn", "chemowks", "CTregmen", "tx"),
measure.vars = measures)
setnames(D, "variable", "Variable")
D <- D[Variable == "tapdomz", Label := "Finger Tapper - Dom Hand"]
D <- D[Variable == "tapndomz", Label := "Finger Tapper - NonDom Hand"]
D <- D[Variable == "pegdomz", Label := "Pegboard - Dom Hand"]
D <- D[Variable == "pegndomz", Label := "Pegboard - Nondom Hand"]
D <- D[Variable == "cowz", Label := "COWAT"]
D <- D[Variable == "bntz", Label := "Boston Naming"]
D <- D[Variable == "trlaz", Label := "Trail Making A"]
D <- D[Variable == "trlbz", Label := "Trail Making B"]
D <- D[Variable == "dsymz", Label := "WAIS-III Digit Symbol"]
D <- D[Variable == "dspaz", Label := "WAIS-III Digit Span"]
D <- D[Variable == "aritz", Label := "WAIS-III Arithmetic"]
D <- D[Variable == "numz", Label := "WAIS-III Number/Letter"]
D <- D[Variable == "reyz", Label := "Rey Copy"]
D <- D[Variable == "bustotz", Label := "Buschke Total "]
D <- D[Variable == "bvrcoz", Label := "Benton Visual Retention Correct"]
setkey(D, subid, session)Exclude
D <- D[chemoyn != "No" &
session != "Pre-surgery"]
D[, .N, .(chemoyn, session)]## chemoyn session N
## 1: Yes Post surgery before treatment 450
## 2: Yes Post treatment/6mths post surgery 450
## 3: Yes 6 month follow-up 375Calculate means and standard deviations
T <- D[,
.(N = .N,
meanZ = mean(value, na.rm=TRUE),
sdZ = sd(value, na.rm=TRUE)),
.(Variable,
Label,
session)]
setkey(T, Variable, Label, session)Check against Table 2, column CT Group of Tager, FA, et al..
T1 <- T[session == "Post surgery before treatment"]
T1 <- T1[, x := sprintf("%.2f (%.2f)", meanZ, sdZ)]
T1[, .(Variable, Label, N, x)]## Variable Label N x
## 1: tapdomz Finger Tapper - Dom Hand 30 1.74 (1.21)
## 2: tapndomz Finger Tapper - NonDom Hand 30 1.38 (1.11)
## 3: pegdomz Pegboard - Dom Hand 30 -0.18 (1.67)
## 4: pegndomz Pegboard - Nondom Hand 30 -0.41 (1.64)
## 5: cowz COWAT 30 0.24 (0.94)
## 6: bntz Boston Naming 30 -0.33 (1.48)
## 7: trlaz Trail Making A 30 0.40 (1.00)
## 8: trlbz Trail Making B 30 0.32 (1.18)
## 9: dsymz WAIS-III Digit Symbol 30 0.69 (0.98)
## 10: dspaz WAIS-III Digit Span 30 0.23 (0.91)
## 11: aritz WAIS-III Arithmetic 30 0.09 (0.92)
## 12: numz WAIS-III Number/Letter 30 0.34 (0.90)
## 13: reyz Rey Copy 30 -1.52 (2.84)
## 14: bustotz Buschke Total 30 -0.60 (1.06)
## 15: bvrcoz Benton Visual Retention Correct 30 0.01 (1.17)Map session to months after treatment. Tager, FA, et al.
As far as I can tell, values of ptime map to these, although seemingly imprecise.
T <- T[session == "Post surgery before treatment", monthsPostTx := 0]
T <- T[session == "Post treatment/6mths post surgery", monthsPostTx := 1]
T <- T[session == "6 month follow-up", monthsPostTx := 6]Merge Kathleen’s KVanDyk@mednet.ucla.edu domain assignments.
From: Van Dyk, Kathleen [KVanDyk@mednet.ucla.edu]
Sent: Tuesday, November 03, 2015 5:08 PM
To: Ayse Tezcan
Cc: Benjamin Chan
Subject: RE: Cognitive impairment draft paper
Hi,
Attached is the Ono spreadsheet with a new column with my suggestions for
domains and domains for each Ahles test is in sheet 2. I've highlighted tests
that we may want to exclude if we want to consistently keep one or two
measures per test. Ben --- does it matter statistically if there is more than
one measure from the same test (for example delayed recall and delayed
recognition) in the same domain? In almost every case we have total and delay
for memory tests but if we add in more measures (Trial 6, Supraspan,
Recognition) does this confound analyses because these are likely highly
correlated measures within the same test? Would all of the studies need to
use the same measures in each test (i.e., every study uses Total and Delay)?
I might not be asking this clearly --- let me know what you think.f <- sprintf("%s/%s", pathIn, "Requested Chemo Data domains kvd 11.19.15 2.xlsx")
echoFile(f)## File: StudyDocuments/CognitiveImpairment/Requested Chemo Data domains kvd 11.19.15 2.xlsx
## Modification date: 2015-11-19 10:55:10
## File size: 178.9 KBD0 <- read.xlsx(f, sheet=1, check.names=TRUE)
D0 <- data.table(D0)
D0 <- D0[First.Auth == "Tager" & !is.na(DOMAIN.FOR.META..kvd.),
.(Label = Cog.Test,
CognitiveDomainForMetaAnalysis = DOMAIN.FOR.META..kvd.)]
D0 <- D0[Label == "WAIS-IIIDigit Span",
Label := "WAIS-III Digit Span"]
lookup <- D0
T <- merge(lookup, T, by="Label")Save working data tables to file.
metadata <- makeMetadata(T)
f <- "Tager.RData"
save(T, metadata, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## Tager.RData saved on: 2015-11-25 15:02:05
## File size: 19.042 KBReshape the Ono data set so it has a similar structure to the Ahles and Tager data.
f <- "Ono.RData"
load(f, verbose=TRUE)## Loading objects:
## D
## metadataD
## DFixed
## metadataDFixed
## DRandom
## metadataDRandommetadataD## $timeStamp
## [1] "2015-11-25 15:02:04 PST"
##
## $objectSize
## [1] "42.3 Kb"
##
## $note
## NULL
##
## $rowCount
## [1] 120
##
## $colCount
## [1] 32
##
## $colNames
## [1] "author" "comparisonGroup"
## [3] "healthyGroup" "treatmentGroup"
## [5] "timeDays" "nGroup1"
## [7] "nGroup2" "nTotal"
## [9] "ageGroup1" "ageGroup2"
## [11] "meanGroup1" "sdGroup1"
## [13] "meanGroup2" "sdGroup2"
## [15] "direction" "randomEffect"
## [17] "CognitiveDomainPrimary" "CogTest"
## [19] "CognitiveDomainForMetaAnalysis" "ScoreTyp"
## [21] "diffMean" "sdPooled"
## [23] "cohenD" "hedgesG"
## [25] "var1" "var2"
## [27] "variance" "se"
## [29] "weightFE" "effSizeWeightedFE"
## [31] "weightRE" "effSizeWeightedRE"
##
## $colClasses
## author comparisonGroup
## "character" "character"
## healthyGroup treatmentGroup
## "numeric" "character"
## timeDays nGroup1
## "numeric" "numeric"
## nGroup2 nTotal
## "numeric" "numeric"
## ageGroup1 ageGroup2
## "numeric" "numeric"
## meanGroup1 sdGroup1
## "numeric" "numeric"
## meanGroup2 sdGroup2
## "numeric" "numeric"
## direction randomEffect
## "character" "numeric"
## CognitiveDomainPrimary CogTest
## "character" "character"
## CognitiveDomainForMetaAnalysis ScoreTyp
## "character" "character"
## diffMean sdPooled
## "numeric" "numeric"
## cohenD hedgesG
## "numeric" "numeric"
## var1 var2
## "numeric" "numeric"
## variance se
## "numeric" "numeric"
## weightFE effSizeWeightedFE
## "numeric" "numeric"
## weightRE effSizeWeightedRE
## "numeric" "numeric"
##
## $sysInfo
## sysname release
## "Windows" "7 x64"
## version nodename
## "build 7601, Service Pack 1" "GHBA299"
## machine login
## "x86-64" "chanb"
## user effective_user
## "chanb" "chanb"
##
## $sessionInfo
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] extrafont_0.17 DiagrammeR_0.7 xtable_1.7-4
## [4] haven_0.2.0 googlesheets_0.1.0 openxlsx_3.0.0
## [7] data.table_1.9.6 devtools_1.8.0 RevoUtilsMath_3.2.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.1 cellranger_1.0.0 formatR_1.2 git2r_0.11.0
## [5] tools_3.2.2 digest_0.6.8 jsonlite_0.9.16 memoise_0.2.1
## [9] evaluate_0.7.2 DBI_0.3.1 rstudioapi_0.3.1 curl_0.9.3
## [13] yaml_2.1.13 parallel_3.2.2 Rttf2pt1_1.3.3 dplyr_0.4.3
## [17] httr_1.0.0 stringr_1.0.0 xml2_0.1.2 knitr_1.11
## [21] htmlwidgets_0.5 rversions_1.0.2 R6_2.1.1 rmarkdown_0.7
## [25] extrafontdb_1.0 magrittr_1.5 htmltools_0.2.6 assertthat_0.1
## [29] stringi_0.5-5 chron_2.3-47Melt data.
idVars <- c("author",
"comparisonGroup",
"treatmentGroup",
"timeDays",
"CogTest",
"CognitiveDomainPrimary",
"CognitiveDomainForMetaAnalysis",
"ScoreTyp")
DN <- melt(D, id.vars = idVars,
measure.vars = c("nGroup1", "nGroup2"), value.name = "N",
na.rm=TRUE)
DMean <- melt(D, id.vars = idVars,
measure.vars = c("meanGroup1", "meanGroup2"), value.name = "mean",
na.rm=TRUE)
DSD <- melt(D, id.vars = idVars,
measure.vars = c("sdGroup1", "sdGroup2"), value.name = "sd",
na.rm=TRUE)Check studies.
D[, .N, .(author, comparisonGroup, treatmentGroup, timeDays)]## author comparisonGroup
## 1: Bender One week after completion of chemotherapy
## 2: Bender One week after completion of chemotherapy
## 3: Bender One year after completion of chemotherapy
## 4: Bender One year after completion of chemotherapy
## 5: Collins NA
## 6: Collins NA
## 7: Jenkins NA
## 8: Jenkins NA
## 9: Wefel Chemotherapy 3 weeks
## 10: Wefel Chemotherapy one year after
## 11: Wefel NA
## treatmentGroup timeDays N
## 1: Chemotherapy + Tamoxifen 182.625 7
## 2: Chemotherapy 182.625 7
## 3: Chemotherapy + Tamoxifen 547.500 7
## 4: Chemotherapy 547.500 7
## 5: Chemotherapy with or without hormone therapy 537.900 21
## 6: Chemotherapy with or without hormone therapy 146.500 21
## 7: Chemotherapy 12 months after chemo 364.000 13
## 8: Chemotherapy 4 weeks after chemo 28.000 13
## 9: NA 182.621 10
## 10: NA 547.863 10
## 11: NA NA 4Prepare measure data sets for merging.
DN <- DN [variable == "nGroup1" , group := "Group 1"]
DMean <- DMean[variable == "meanGroup1", group := "Group 1"]
DSD <- DSD [variable == "sdGroup1" , group := "Group 1"]
DN <- DN [variable == "nGroup2" , group := "Group 2"]
DMean <- DMean[variable == "meanGroup2", group := "Group 2"]
DSD <- DSD [variable == "sdGroup2" , group := "Group 2"]Merge the melted data.
setkeyv(DN, c(idVars, "group"))
setkeyv(DMean, c(idVars, "group"))
setkeyv(DSD, c(idVars, "group"))
D <- merge(DN[, variable := NULL], DMean[, variable := NULL])
D <- merge(D, DSD[, variable := NULL])Deduplicate pre-treatment data.
D1 <- D[group == "Group 1"]
setkeyv(D1, idVars[!(idVars %in% c("comparisonGroup", "treatmentGroup", "timeDays"))])
D1 <- unique(D1)
D1 <- D1[, monthsPostTx := 0]
D1 <- D1[,
`:=` (comparisonGroup = NULL,
treatmentGroup = NULL,
timeDays = NULL,
group = NULL)]Calculate monthsPostRx for post-treatment values.
D2 <- D[group == "Group 2"]
D2 <- D2[, monthsPostTx := round(timeDays / 365.25 * 12)]
D2 <- D2[,
`:=` (comparisonGroup = NULL,
timeDays = NULL,
group = NULL)]rbind pre-treatment and post-treatment data.
D <- rbind(D1, D2, fill=TRUE)Check data structure
D[, .N, .(author, monthsPostTx)]## author monthsPostTx N
## 1: Bender 0 7
## 2: Collins 0 23
## 3: Jenkins 0 13
## 4: Wefel 0 10
## 5: Bender 6 14
## 6: Bender 18 14
## 7: Collins 5 21
## 8: Collins 18 21
## 9: Jenkins 12 13
## 10: Jenkins 1 13
## 11: Wefel 6 10
## 12: Wefel 18 10Overwrite the data to file.
metadata <- makeMetadata(D)
f <- "Ono.RData"
save(D, metadata, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## Ono.RData saved on: 2015-11-25 15:02:05
## File size: 20.151 KBLoad data from
f <- "Ono.RData"
load(f, verbose=TRUE)## Loading objects:
## D
## metadatametadata$colNames## [1] "author" "CogTest"
## [3] "CognitiveDomainPrimary" "CognitiveDomainForMetaAnalysis"
## [5] "ScoreTyp" "N"
## [7] "mean" "sd"
## [9] "monthsPostTx" "treatmentGroup"D1 <- D
colNames1 <- metadataD$colNames
f <- "Ahles.RData"
load(f, verbose=TRUE)## Loading objects:
## D
## metadatametadata$colNames## [1] "Variable" "CognitiveDomainForMetaAnalysis"
## [3] "txgrp" "ptime"
## [5] "NObs" "Label"
## [7] "N" "Mean"
## [9] "Median" "StdDev"
## [11] "monthsPostTx"D2 <- D
f <- "Tager.RData"
load(f, verbose=TRUE)## Loading objects:
## T
## metadatametadata$colNames## [1] "Label" "CognitiveDomainForMetaAnalysis"
## [3] "Variable" "session"
## [5] "N" "meanZ"
## [7] "sdZ" "monthsPostTx"D3 <- TStructure of the data should be
authormonthsPostTxtreatmentGroupcognitiveDomainOriginal (remove this column since we won’t use it)cognitiveDomaincognitiveTestscoreTypenmeansdRestructure Ono.
colOrder <- c("author",
"monthsPostTx",
"treatmentGroup",
"cognitiveDomain",
"cognitiveTest",
"scoreType",
"n",
"mean",
"sd")
D1 <- D1[author == "Wefel", author := "Wefel 2004"]
setnames(D1,
c("CogTest", "CognitiveDomainPrimary", "CognitiveDomainForMetaAnalysis", "ScoreTyp", "N"),
c("cognitiveTest", "cognitiveDomainOriginal", "cognitiveDomain", "scoreType", "n"))
D1[,
`:=` (cognitiveDomainOriginal = NULL)]
setcolorder(D1, colOrder)Restructure Ahles.
D2 <- D2[, author := "Ahles"]
setnames(D2,
c("CognitiveDomainForMetaAnalysis", "txgrp", "Label", "N", "Mean", "StdDev"),
c("cognitiveDomain", "treatmentGroup", "cognitiveTest", "n", "mean", "sd"))
D2[,
`:=` (Variable = NULL,
ptime = NULL,
NObs = NULL,
Median = NULL,
scoreType = "Z-score")]
setcolorder(D2, colOrder)Restructure Tager.
D3 <- D3[, author := "Tager."]
setnames(D3,
c("Label", "CognitiveDomainForMetaAnalysis", "N", "meanZ", "sdZ"),
c("cognitiveTest", "cognitiveDomain", "n", "mean", "sd"))
D3[,
`:=` (Variable = NULL,
session = NULL,
treatmentGroup = "Chemo",
scoreType = "Z-score")]
setcolorder(D3, colOrder)rbindlist the data.
D <- rbindlist(list(D1, D2, D3))Save working data tables to file.
metadata <- makeMetadata(D)
f <- "AllStudies.RData"
save(D, metadata, file=f)
message(sprintf("%s saved on: %s\nFile size: %s KB",
f,
file.mtime(f),
file.size(f) / 1e3))## AllStudies.RData saved on: 2015-11-25 15:02:06
## File size: 23.302 KB## Sourcing https://gist.githubusercontent.com/benjamin-chan/80149dd4cdb16b2760ec/raw/a1fafde5c5086024dd01d410cc2f72fb82e93f26/sessionInfo.R
## SHA-1 hash of file is 41209357693515acefb05d4b209340e744a1cbe4## $timeStart
## [1] "2015-11-25 15:02:03"
##
## $timeEnd
## [1] "2015-11-25 15:02:06 PST"
##
## $timeElapsed
## [1] "2.538 secs"
##
## $Sys.info
## sysname release
## "Windows" "7 x64"
## version nodename
## "build 7601, Service Pack 1" "GHBA299"
## machine login
## "x86-64" "chanb"
## user effective_user
## "chanb" "chanb"
##
## $sessionInfo
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 7 x64 (build 7601) Service Pack 1
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] extrafont_0.17 DiagrammeR_0.7 xtable_1.7-4
## [4] haven_0.2.0 googlesheets_0.1.0 openxlsx_3.0.0
## [7] data.table_1.9.6 devtools_1.8.0 RevoUtilsMath_3.2.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.1 cellranger_1.0.0 formatR_1.2 git2r_0.11.0
## [5] tools_3.2.2 digest_0.6.8 jsonlite_0.9.16 memoise_0.2.1
## [9] evaluate_0.7.2 DBI_0.3.1 rstudioapi_0.3.1 curl_0.9.3
## [13] yaml_2.1.13 parallel_3.2.2 Rttf2pt1_1.3.3 dplyr_0.4.3
## [17] httr_1.0.0 stringr_1.0.0 xml2_0.1.2 knitr_1.11
## [21] htmlwidgets_0.5 rversions_1.0.2 R6_2.1.1 rmarkdown_0.7
## [25] extrafontdb_1.0 magrittr_1.5 htmltools_0.2.6 assertthat_0.1
## [29] stringi_0.5-5 chron_2.3-47